An analytical and optimization framework on rate-Cramér-Rao bound (CRB) tradeoff is proposed in this paper for the continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) system. To evaluate the dual-functional performance, the sensing CRB and communication rate are analyzed concerning the induced electromagnetic (EM) waves of CAPAs. For rate-CRB region characterization, the spatially continuous beamforming of transmit CAPA is optimized under three cases: i) A novel closed-form expression for the optimal CAPA beamformer is derived under the single-user single-target scenario, proven to be aligned within the space spanned by the EM-based sensing and communication channels; ii) A general subspace-based beamforming design approach is proposed to address the intractable continuity, converting the continuous beamforming design in spatial domain to discrete weight design in subspace domain and resorting to the semidefinite relaxation for the globally optimal solution; iii) Moreover, the general beamforming design is specialized to both the low-complexity zero-forcing (ZF) and the conventional spatially discrete array (SPDA)-based designs. Numerical results demonstrate that: i) The proposed subspace-based approach can realize efficient and effective beamforming design for reduced mutual interference, enhanced sensing performance, and guaranteed communication rate; ii) The general CAPA beamforming design achieves broader rate-CRB region than the ZF-oriented design and reaches the ultimate performance of the SPDA-based system.

As an emerging communication technology, fluid antenna (FA) offers remarkable diversity and multiplexing gains due to its port mobility, which significantly reduces transmission delays in communication processes. This capability makes FA a promising solution for enhancing mobile edge computing (MEC) by optimizing communication delay. This paper establishes an FA-aided MEC offloading architecture and proposes a game theory-assisted multi-agent deep reinforcement learning (DRL) scheme to minimize the system delay of MEC. We aim to address the joint optimization problem of FA port selection, beamforming, user transmit power design, and MEC server computation resource allocation. However, the dynamic nature of FA ports and the variability of the associated large number of parameters introduce significant challenges, such as non-convexity and high dimension, in the optimization problem. In this paper, we employ game theory to reduce the dimension of the optimization variables by modeling the power control problem among multiple users as a non-cooperative game. Therefore, we propose a multi-agent deep deterministic policy gradient (MADDPG) algorithm, featuring two types of agents that collaboratively solve the problem. Simulation results validate the effectiveness of the proposed scheme, achieving 19.1-65.8% lower delays than benchmarks in MEC efficiency across all scenarios.

Energy efficiency (EE) is vital for 5G networks to manage the increased traffic demand while minimizing operational costs and reducing environmental impact. Optimizing energy usage also supports scalability and helps meet regulatory sustainability goals as network infrastructure expands. In a practical commercial 5G network, a widely-used method to reduce energy consumption is to dynamically shut down underutilization cells and reduce the overlapping cell coverage based on real-time traffic patterns. However, the inherent uncertainty of traffic demands, coupled with the unknown distribution function, significantly complicates the optimization of cell shutdown strategies, rendering both conventional deterministic and stochastic optimization methods ineffective. To tackle these challenges, we propose a distributionally robust optimization framework for EE optimization that does not rely on distributional knowledge. First, we construct a data-driven uncertainty set to model traffic distribution and apply Lagrangian duality to transform the infinite-dimensional optimization problem into a more tractable finite-dimensional one. Then, we employ a Bayesian optimization algorithm to efficiently solve this reformulated problem, which involves a mixed space of high-dimensional combinatorial cell shutdown and continuous Lagrangian multipliers, even with constrained black-box evaluations. Simulations on real-world field data show that our proposed solution outperforms existing benchmarks, achieving energy efficiency improvements ranging from 0.7 % to 11.05 %.

Federated learning (FL) with synchronous model aggregation suffers from the straggler issue because of heterogeneous transmission and computation delays among different agents. In mobile wireless networks, this issue is exacerbated by time-varying network topology due to agent mobility. Although asynchronous FL can alleviate straggler issues, it still faces critical challenges in terms of algorithm design and convergence analysis because of dynamic information update delay (IU-Delay) and dynamic network topology. To tackle these challenges, we propose a decentralized FL framework based on gradient descent with momentum, named decentralized momentum federated learning (DMFL). We prove that DMFL is globally convergent on convex loss functions under the bounded time-varying IU-Delay, as long as the network topology is uniformly jointly strongly connected. Moreover, DMFL does not impose any restrictions on the data distribution over agents. Extensive experiments are conducted to verify DMFL’s performance superiority over the benchmarks and to reveal the effects of diverse parameters on the performance of the proposed algorithm.

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints.

This paper investigates a wireless powered internet of things (IoT) network, where a hybrid access point (HAP) performs both wireless energy transfer (WET) and wireless information transfer (WIT). Specifically, the HAP charges IoT devices via WET and collects their updated information via WIT. To ensure the information freshness, an adaptive cooperative transmission scheme is proposed, where information packets of devices are transmitted either directly or cooperatively with the assistance of another device. To this end, an expected weighted sum of age of information (EWSAoI) minimization problem is formulated to adaptively determine the best device pairs for cooperative transmissions, and schedule the corresponding transmission, i.e., WET, direct WIT, or cooperative WIT. Leveraging the Lyapunov optimization framework, a low-complexity adaptive scheduling scheme is proposed, wherein the device pairing or information transmissions are determined based on the instantaneous AoI and energy status of IoT devices. Furthermore, to reduce signalling overhead, a two-timescale scheduling scheme is proposed, wherein a matching algorithm is incorporated to pair devices for cooperative transmission in a large timescale while the adaptive transmission of energy and information are executed in a small timescale. Simulation results validate that adaptive cooperative scheduling scheme effectively reduces the AoI with low system overhead, and the gain approaches to 43.3%, compared with non-cooperative scheme.

This paper investigates a wireless-powered Internet of Things (IoT) network comprising a hybrid access point (HAP) and two devices. The HAP facilitates downlink wireless energy transfer (WET) for device charging and uplink wireless information transfer (WIT) to collect status updates from the devices. To keep the information fresh, concurrent WET and WIT are allowed, and orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are adaptively scheduled for WIT. Consequently, we formulate an expected weighted sum age of information (EWSAoI) minimization problem to adaptively schedule the transmission scheme, choosing from WET, OMA, NOMA, and WET+OMA, and to allocate transmit power. To address this, we reformulate the problem as a Markov decision process (MDP) and develop an optimal policy based on instantaneous AoI and remaining battery power to determine scheme selection and transmit power allocation. Extensive results demonstrate the effectiveness of the proposed policy, and the optimal policy has a distinct decision boundary-switching property, providing valuable insights for practical system design.

By controlling the propagation environment, intelligent reflecting surface (IRS) improve the channel quality, and becomes a promising technique. Meanwhile, movable antenna (MA) shows great potential to enhance the received signal-noise-ratio (SNR) by configuring antenna positions. In this paper, we exploit the advantages of both techniques, and study a MA and IRS enhanced wireless powered internet of things (IoT) network, wherein a hybrid access point (HAP) charges MA-enabled IoT devices via wireless energy transfer (WET) technology, and devices utilize the harvested energy to upload their information to the HAP. Basically, a network throughput maximization (NTM) problem is formulated to jointly optimize the IRS reflecting beamforming, the time allocation subject to total time constraint, and the MA position control subject to MA's feasible region constraints. Concerning the non-convexity of the NTM problem, we exploit the block coordinate ascent (BCA) approach to divide it into reflecting beamforming and time allocation sub-problem, and MA position control sub-problem, which are independently and iteratively solved until the solution of original problem is converged. For the reflecting beamforming and time allocation optimization sub-problem, the successive convex approximate (SCA) algorithm is used to transform it into a convex problem. For the MA position control sub-problem, we transform it into a convex mixed integer non-linear programming (MINLP) problem. Finally, extensive simulation results demonstrate the proposed approach for IRS-assisted wireless powered IoT network with MA can significantly improve the network throughput, where the performance gain is over 127%, compared with IRS-assisted wireless powered IoT networks.

Intelligent Reflecting Surface (IRS) enabled Unmanned Aerial Vehicle (UAV) edge computing, a new communication technology, can provide sufficient capacity for edge computing system. However, due to the Line-of-Sight (LoS) or the Non Line of Sight (NLoS) of communicating environments will impact transmitting rate or delay, the Intelligent Reflective Surface (IRS) can be utilized to compensate the channel fading in the IRS-enabled UAV edge computing. In this paper, the joint problem of IRS phase shift, UAV trajectory and power allocation in the system is investigated, aiming to maximize the energy efficient. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To solve the problem, the original problem is decomposed into two subproblems, and an iterative method framework based on ConVex optimization and Deep Reinforcement Learning (CV-DRL) is proposed. Given the UAV trajectory and IRS phase shift, the Convex optimization algorithm is used to solve the power allocation schemes. Then, given the power allocation schemes, the Double Deep Q Network (Double DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are utilized to solve the problem of optimal UAV trajectory and IRS phase shift. The simulation results demonstrate that our proposed method outperforms other schemes in terms of energy efficiency, providing significant enhancements

Unmanned aerial vehicle (UAV)-enabled mobile-edge computing (MEC) is expected to provide low-latency, ultrareliable, and highly robust network services to improve user service experience. In this article, the UAV deployment, task offloading, and resource allocation problem is investigated in a multi-UAV-enabled MEC system with task-intensive region. UAVs as edge servers to provide computing services for ground terminal devices (TDs). The time-sensitive tasks of TDs can be computed locally or offloaded to UAVs. The goal is to improve the utility of tasks, i.e., maximize the number of tasks offloaded to UAVs under conditions of ensuring a desired task computed success rate and satisfying the energy and latency constraints. The jointly optimizing problem of the 3-D deployment, elevation angle, computational resource allocation of the UAV, and task offloading decision is formulated. To this end, a two-layer optimization approach is proposed to solve the formulated problem. Specifically, the upper layer decides the UAV position, elevation angle, and transmission power of TDs based on the actual ground situation. The lower layer determines the computational resource allocation of UAVs and the task offloading decision based on the optimized results derived from the upper layer. Through the two-layer joint optimization, our goal is finally achieved. Simulation results demonstrate that our proposed algorithm effectively improves the number of tasks offloaded to UAVs and the task completion rate simultaneously with the flexible UAV deployment and well-designed task offloading strategy.

In this paper, we aim to minimize the age of information (AoI) for a random access internet of things (IoT) network, where AoI is a metric to measure the freshness of information delivery. Since non-orthogonal multiple access (NOMA) can improve network throughput and connectivity, we exploit an AoI-oriented NOMA-based random access scheme, wherein devices simultaneously access wireless channel over multiple power levels with different access probabilities when their AoIs is not smaller than a threshold. We firstly study the comprehensive steady-state analysis of an AoI-independent NOMA-based random access scheme, which is a special case when the threshold is one. The AoI evolution is formulated as a markov chain based on the analyzed transmission success probability, and the probabilities of AoI states and the achieved AoI under generate-at-will are derived. Then, an AoI minimization algorithm is proposed to optimize the power access probabilities. Concerning stochastic-arrival, the steady-state probabilities of devices’ active state, successful transmission, and number of active devices, are derived to analyze the expected AoI. Finally, the steady-state probabilities of AoI states and the achieved AoI of AoI-dependent NOMA-based scheme are obtained. Simulation results validate our analysis, and demonstrate the significant performance improvement in terms of AoI. In specific, the proposed scheme can achieve AoI reduction by 65%, compared with random access without NOMA.

We consider a mobile edge computing (MEC) assisted Industrial Internet of Things (IIoT) network, where multiple assembly processing lines in a smart factory are equipped with sensing devices. They sense raw products, generate and offload computing tasks, and finally process the raw products based on the computing results. In this scenario, different positions of the processing machines lead to different priorities and diverse Quality-of-Service (QoS) requirements of tasks. Therefore, how to schedule tasks and allocate the network resources becomes a critical and challenging issue. In this study, we introduce a novel batch-based hybrid nonorthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission scheme. The selection between NOMA and OMA schemes is optimized based on the QoS requirements of tasks. Then, we formulate a production efficiency maximization problem with the objective of maximizing the speed of the assembly lines subject to the deadline constraints of offloading and computing procedures. To this end, a two-layer decomposition method is used to decompose the formulated problem into two subproblems. Furthermore, we utilize a bisection searching method to approximate the optimal solution, and propose an efficient method to determine the feasibility of the top-layer subproblem. Simulation results demonstrate the significant performance improvement of our proposed method. In specific, the production efficiency is enhanced by 525% in comparison with pure NOMA scheme.

Compared with traditional half-duplex wireless systems, the application of emerging full-duplex (FD) technology can potentially double the system capacity theoretically. However, conventional techniques for suppressing self-interference (SI) adopted in FD systems require exceedingly high power consumption and expensive hardware. In this paper, we consider employing an intelligent reflecting surface (IRS) in the proximity of an FD base station (BS) to mitigate SI for simultaneously receiving data from uplink users and transmitting information to downlink users. The objective considered is to maximize the system weighted sum-rate by jointly optimizing the IRS phase shifts, the BS transmit beamformers, and the transmit power of the uplink users. To visualize the role of the IRS in SI cancellation, we first study a simple scenario with one downlink user and one uplink user. To address the formulated non-convex problem, a low-complexity algorithm based on successive convex approximation is proposed. For the more general case considering multiple downlink and uplink users, an efficient alternating optimization algorithm based on element-wise optimization is proposed. Numerical results demonstrate that the FD system with the proposed schemes can achieve a larger gain over the half-duplex system, and the IRS is able to achieve a balance between suppressing SI and providing beamforming gain.

Mobile edge caching is promising to improve content delivery and alleviate backbone burden by caching contents at the network edges. The commercial deployment relies on a comprehensive understanding of the economic interactions involved. This paper studies the edge caching enabled Internet ecosystem including a Content Provider (CP), a Global ISP (G-ISP) providing backbone services, a Local ISP (L-ISP) providing access services, and End-Users (EUs). The CP serves EUs via Internet servers or L-ISP's edge cache. We formulate their multi-tiered interactions as a three-stage dynamic game. In Stage I, CP determines edge cache storage to purchase from L-ISP and cache access fee to charge EUs. In Stage II, G-ISP and L-ISP determine backbone and access prices. In Stage III, EUs decide whether to choose edge cache services, considering cache hit probability, cache access fee, and backbone access prices. We analyze the subgame perfect equilibrium by elaborately designing five cases of EUs' choices, five regions of ISPs' pricing, and three patterns of CP's caching, undercooperativeandcompetitiveISP pricing scenarios. Our analysis demonstrates that adopting edge caching leads to win-win outcomes for all parties involved. Furthermore, we find that competitive pricing is more advantageous for CP's profit when cache costs are low, while cooperative pricing is more beneficial when cache costs are high.

With the advantages of high mobility and flexible deployment, Unmanned Aerial Vehicle (UAV) combines with Mobile Edge Computing (MEC) is a promising technology. When dynamic Terminal Users (TUs) offload tasks to UAVs, eavesdroppers may eavesdrop on the channel information. The offloading decisions, trajectory plannings of UAVs and resource allocation with the objective of high-capacity secure communication is a challenging problem. In this paper, we design a multi-UAVs MEC system, where the original region is divided into several sub-regions and TUs offload tasks to UAVs which provide computing services for these TUs. Meanwhile, A joint optimization problem of offloading decision, resource allocation and trajectory planning is formulated, where TUs move with the Gauss-Markov random model. In addition, the Base Station (BS) emits jamming signals to evade the eavesdropping of offloading information from eavesdroppers. The goal of the optimization problem is to maximize the TUs’ minimum secure calculation capacity, and a Joint Dynamic Programming and Bidding (JDPB) algorithm is proposed to solve it. The Successive Convex Approximation (SCA) and Block Coordinate Descent (BCD) algorithms are used to handle the resource allocation and trajectory planning problems, and the bidding method is used to address the task offloading decision problem. Simulation results show that JDPB has better performance and better robustness under different parameter settings than other schemes.

In China, some e-commerce platform (EP) companies such as Alibaba and JD are now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players, with EP companies being VNOs, through appropriate integration of the VNO business and the companies' own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage static Stackelberg game. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Besides, for the scenario lack of knowledge on the interaction between the NO and VNO in a dynamic game, we propose a deep Q-network (DQN) based algorithm to derive the optimal strategies of the NO and VNO. Simulation results show impact of system parameters on the utilities of game players and social welfare. We also study the impact of system parameters on different algorithms and discover that the proposed DQN-based algorithm can learn a good strategy as compared with the Stackelberg equilibrium solution.

The paradigm of upcoming 5G and beyond is the massive machine type communications (mMTC), where a large number of devices automatically operate wireless communications. Due to their automatic and energy-intensive operations, energy efficiency (EE) becomes crucial, but there is a lack of investigation for EE of random access networks, which is the underlying platform for mMTC. In this paper, we focus on the EE of carrier sense multiple access-based non-orthogonal multiple access (NOMA) random access networks. We first construct mathematical models. Instead of investigating all combinations regarding successful decoding events as in previous works, by pivoting around the decoding process of a specific signal, a hidden pattern of NOMA decoding process is unveiled, which can largely decrease analytical complexity. Then, together with this feature, by adopting Markov chain and Q-function approximation, closed-form formulation for EE is derived. Subsequently, to efficiently solve the complicated non-convex EE maximization problem built via the constructed models, we employ an approach that unifies complementary geometric programming (CGP) and difference of convex programming (DCP) to optimize all the controllable parameters at device side, namely, transmission probability, power, and data rate, with a tightest lower bound strategy to guarantee seamless EE improvement and very fast convergence to local optimal points even in the worst case. Simulation experiments verify the accuracy of mathematical models and efficiency of optimization scheme.

Caching at the wireless edge is recognized as a promising solution to accommodate the explosive growth of traffic demand. However, the gain of edge caching is only pronounced given homogeneous user preference. To reap the full potential of caching, recommendation mechanism has emerged as an attractive technology due to its capability of reshaping users’ request distribution. In this work, we propose a joint user-side recommendation and device-to-device (D2D)-assisted offloading strategy, aiming to maximize the operator’s utility. Specifically, we consider that users can recommend their cached contents to encountered users. This strategy takes into account users’ personalized preferences and relative locations, and hence can directly offload the recommended contents through D2D links without burdening cellular links. We then develop a theoretical framework to evaluate the subsequent content transmission, accounting for the randomness of spatial deployment, user mobility, individual delay requirement, incentive, and protection mechanism for existing links. Based on the analytical results, we design a D2D-assisted offloading strategy, which allows the requester to postpone data reception in exchange for discounted service fees. Simulation results show that the operator’s utility can be significantly improved. Particularly, it is found that user mobility facilitates the above process.

Automated driving technology has attracted extensive interests in both academia and industry. High-definition (HD) map plays the critical basis in ensuring the accuracy perception and localization for automated driving. Considering the time-sensitivity, location and direction-dependency, the dissemination of HD map is a challenging task. In this paper, we address the efficient transmission of HD map data from road side units (RSUs) to vehicles passing through their communication coverage. By jointly considering the age of information (AoI) and the direction of information (DoI) for all vehicles, we propose a deep-reinforcement learning based method to achieve efficient dissemination of HD map data, where we use the deep reinforcement learning to optimize the selection of target vehicles, then optimize the power allocation among all selected vehicles. Simulation results show the performance improvement of our proposed method compared with several existing counterparts. In specific, our proposed method can improve the ratio of vehicles that obtains the map data of the next area by 229% compared with random scheme with the number of vehicles n=4.

Mobile crowdsensing (MCS) booms the implementation of the Internet of Things (IoT) in different areas due to flexibility and low deployment cost. However, collecting sufficient high quality sensing data is crucial for the success of various applications. Incentive mechanism design plays a critical role in the successful implementation of mobile MCS systems. Most of existing work consider that the platform exactly knows the trajectory of mobile users. However, in most cases, it is difficult to obtain the accurate information of the location of mobile users due to either privacy issue or the lack of information. In this article, we consider nondeterministic mobility of mobile users, where only the probability distribution of users’ mobility is available. We design an effective mechanism to achieve the quality data collection with the objective of maximizing the expected social welfare. Simulation results show that the proposed mechanism achieves her expected social welfare compared with four existing schemes, while satisfying truthfulness, individual rationality, and computational efficiency.

Ultrareliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) are two types of services with delay Quality-of-Service (QoS) demands. Considering the random and sporadic URLLC packets arrival feature, slicing puncture is believed to be the suitable method to support coexistence communication scenario of eMBB and URLLC terminals. However, slicing puncture in a short time is not trivial, and needs accurate wireless resource scheduling. At the same time, it may damage the QoS of eMBB service. All of these make the QoS guaranteed scheduling problem more challenging. In this article, we investigate the bandwidth, power allocations, slice puncturing problems to find the way satisfying services’ individual QoS demands. For the scheduling of eMBB service, we formulate a joint optimization problem for bandwidth and power allocations with long-term constraints of queues backlog. To solve this problem, we utilize the Lyapunov drift-plus-penalty (DPP) method to establish the relationship between the long-term constraints and the short-term optimization problem, which means that the long-term constraints are gradually satisfied by the proposed strategy at each step. We further divide the short-term optimization problem into two subproblems and adopt a block coordinate descent (BCD) algorithm to reduce the computation complexity. Then, we put forward a one-to-one matching method to solve the integer programming in resource block (RB) allocation and slicing puncture problems. Numerical results demonstrate that the proposed dynamic resource allocation and puncturing strategy (DRAPS) can solve the scheduling problem of eMBB and URLLC services in the presence of multiple randomness of channel-state information (CSI) and URLLC packet arrival.

The paradigm of the upcoming 5G and beyond is the machine type communications (MTC), where a large number of devices automatically operate wireless communications. Due to their automatic and energy-intensive operations, energy efficiency (EE) becomes very important, but there is a lack of investigation for EE of random access networks, which is the underlying platform for MTC. In this paper, we focus on the EE of random access orthogonal and non-orthogonal multiple access (OMA and NOMA) networks. We first construct mathematical models of their EE performances. Information-theoretic theories are employed to transform probabilistic decodability into deterministic form for OMA, and along with the unique features of NOMA decoding process, by pivoting around the decodability of a specific packet, we realize the modeling for NOMA. Then, based on the established models, exploiting the nature of random access networks, a complementary geometric programming approach is used for performance optimization. From our investigation, the following results are discovered: (1) NOMA has significant advantage over OMA in EE performance due to its strong data recovery; (2) to maximize the EE performance, devices with higher data rate should transmit more frequently, but with lower transmission power; (3) lower data rates have beneficial effects on EE.

The emerging uplink (UL) and downlink (DL) decoupled radio access networks (RAN) has attracted a lot of attention due to the significant gains in network throughput, load balancing and energy consumption, etc. However, due to the diverse vehicular service requirements in different vehicle-to-everything (V2X) applications, how to provide customized cellular V2X services with diversified requirements in the UL/DL decoupled 5G and beyond cellular V2X networks is challenging. To this end, we investigate the feasibility of UL/DL decoupled RAN framework for cellular V2X communications, including the vehicle-to-infrastructure (V2I) communications and relay-assisted cellular vehicle-to-vehicle (RAC-V2V) communications. We propose a two-tier UL/DL decoupled RAN slicing approach. On the first tier, the deep reinforcement learning (DRL) soft actor-critic (SAC) algorithm is leveraged to allocate bandwidth to different base stations. On the second tier, we model the QoS metric of RAC-V2V communications as an absolute-value optimization problem and solve it by the alternative slicing ratio search (ASRS) algorithm with global convergence. The extensive numerical simulations demonstrate that the UL/DL decoupled access can significantly promote load balancing and reduce C-V2X transmit power. Meanwhile, the simulation results show that the proposed solution can significantly improve the network throughput while ensuring the different QoS requirements of cellular V2X.

Most of existing works about data offloading do not consider the participation of mobile subscribers (MSs) when designing the budget allocation, such that the fairness performance is challenging. For a mobile data offloading system consisting of a base station run by a service provider (SP), multiple MSs, and several third-party WiFi access points (APs), in this paper we study how the SP allocates the budget among APs and arranges the offloading data rate for MSs such that the fairness of each MS’s profit is guaranteed. By jointly considering the preferences of MSs for different APs and budget limit, we propose a two-phase participatory budget and rate allocation (PBRA) scheme where a core solution is designed for the budget allocation in the first phase to guarantee the fairness of all MSs, and an optimal rate allocation based on the core solution is designed to maximize the expected amount of data offloading in the second phase. Simulation results demonstrate the efficacy of our proposed PBRA scheme. Specifically, our proposed scheme can achieve the fairness among all MSs with a less performance loss in terms of the expected amount of data offloading by comparing with three benchmark schemes.

Most of the existing work about device-to-device (D2D) data offtoading do not simultaneously consider the mobile scenario and the trade-off between the revenue and cost of caching data. In this paper, we consider a more comprehensive and practical scenario of D2D data offloading, and design a mobility-aware incentive mechanism to efficiently select some mobile users to pre-cache the proper contents with the objective of maximizing social welfare by jointly considering mobile users' preference similarity and the social relationship. Simulation results demonstrates that the proposed mechanism outperforms other counterparts. In addition, the proposed mechanism satisfies the nice properties of individual rationality and truthfulness.

In Mobile Crowd Sensing (MCS), mobile users usually can be both the contributor of sensing data and the customer of the service provided by the data collector or service provider. Most of existing work does not consider the dual-role of mobile users in MCS. In this work, by jointly considering users' dual-role as both the contributor of sensing data and the customer of service, we design a Lyapunov based online algorithm to achieve the stability of dynamic MCS system while maximizing the platform utility. Meanwhile, we demonstrate the impacts of parameter on the balance of these two roles of mobile users. Simulation results show that the proposed method outperforms some existing methods in terms of achieved platform utility and system stability. In addition, it is proved that, the time-averaged platform utility could converge to the maximum benchmark asymptotically.

Mobile-edge computing (MEC) is a promising technology, which allows reducing latency and energy consumption, thereby making the user experience better. Although MEC can support various types of services, differentiated Quality-of-Service (QoS) requirements bring difficulties and challenges to the allocation of radio resources and computing resources of the MEC system. In this article, we jointly optimize subchannel allocation, as well as the local central processing unit (CPU) speed scaling, user association, subcarrier assignment, power allocation, and video quality decision for MEC systems to study the total cost saving problem. Considering the traffic variations, we develop an online algorithm by using the Lyapunov optimization technique to solve this problem, referred to as dynamic subchannel allocation and resource allocation (DSARA). Particularly, the proposed DSARA algorithm only needs to track the state of the current network without requiring any prior knowledge. Besides, we prove that our proposed algorithm can asymptotically achieve the minimum total cost value (such as minimizing the power consumption and maximizing quality satisfaction). Simulation results show that the DSARA can achieve a good tradeoff between the total cost and delay, and outperforms the existing schemes in terms of the total cost expenditure.

As an important application scenario of the industrial Internet of things, the Internet of Vehicles can significantly improve road safety, improve traffic management efficiency, and improve people's travel experience. Due to the high dynamics of the Internet of vehicles environment, the traditional resource optimization technologies cannot meet the requirements of the Internet of vehicles for dynamic communication, computing and storage resources optimization management, and artificial intelligence algorithms can adaptively obtain dynamic resource allocation schemes through self-learning. Therefore, adopting artificial intelligence techniques to optimize the dynamic resource of the Internet of Vehicles is the research focus of this article. In this article, we first model the Internet of Vehicles resource allocation problem as a semi-Markov decision process that introduces a resource reservation strategy and a secondary resource allocation mechanism. Then, the reinforcement learning algorithm is used to solve the model. Thereafter, it theoretically analyzes the joint optimization of computing and communication resources, models it as a hierarchical architecture, and uses hierarchical reinforcement learning to obtain the optimal system resource allocation plan. Finally, the results of simulation experiments show that the dynamic resource allocation scheme of the Internet of vehicles based on the reinforcement learning in this article greatly improve resource utilization and user quality of experience with guaranteeing system quality of service compared with the traditional greedy algorithm.

With the rich set of embedded sensors installed in smartphones, we are witnessing the emergence of many innovative commercial mobile crowdsensing applications, which combine the power of mobile technology with crowdsourcing to effectively collect time-sensitive and location-dependent information. Motivated by these real-world applications, we consider the distributed task selection problem for heterogeneous users with different initial locations, destinations, costs, speeds, and reputation levels. We design a Bayesian asynchronous task selection (BATS) algorithm to help the users plan their task selections based on the incomplete information of the task popularity statistics. We prove its convergence and characterize the computation time for the users' updates. As a performance benchmark, we consider the ideal case that the service provider centrally allocates the tasks to the users for social surplus maximization. We show that it is an NP-hard problem and propose a greedy centralized algorithm with a lower complexity as the benchmark performance. Simulation results suggest that the BATS scheme achieves the highest Jain's fairness index and coverage, while yielding a user payoff similar to that with the greedy centralized benchmark. Finally, we evaluate the schemes based on some real-world movement time and distance data from Google Maps.

Channel aggregation (CA) is proposed in IEEE 802.11ax to allow wireless users to aggregate multiple available channels, either contiguous or non-contiguous, to improve the network throughput. In this paper, the performance of CA is extensively investigated. It is shown that a simple CA that aggregates all available channels does not always promote but may degrade the network performance due to the increased inter-channel contentions in a random access wireless local area network (WLAN). Thus, it is of critical importance to select an appropriate set of channels for CA. To this end, we propose an efficient probabilistic channel aggregation scheme to maximize the network throughput under the quality of service constraints. That is, an ax user aggregates each secondary channel with a certain probability based on the traffic load of the secondary channel. A Proximal Policy Optimization (PPO) based approach is further applied to intelligently tune the aggregating probabilities of secondary channels to maximize the network throughput. Numerical results show that the proposed algorithm can greatly improve the network throughput compared with existing CA algorithms in the literature.

Mobile edge caching is a promising approach for enhancing content delivery efficiency and alleviating backbone network burden, via caching popular contents at network edge devices (e.g., base stations or WiFi access points). The successful commercial deployment relies on a comprehensive understanding of the economic interactions among different stakeholders involved. In this paper, we study an edge caching system consisting of a Content Provider (CP), an Internet Service Provider (ISP) who provides the backbone network service, a wireless Access Provider (AP) who provides the wireless access service, and a set of mobile End-Users (EUs), where the CP provides contents for EUs either via the remote server (on the Internet) or via the edge cache (purchased from the AP). We formulate their interactions as a three-stage Stackelberg game. In Stage I, the CP decides the edge cache space to purchase from the AP and cache access fee to charge EUs. In Stage II, the ISP and AP determine the backbone and wireless access service prices, respectively. In Stage III, EUs decide whether to subscribe to the CP' s edge cache service, taking the cache hit probability, cache access fee, backbone and wireless access prices into consideration. We analyze the subgame perfect equilibrium of the dynamic game systematically under two different network pricing scenarios: cooperative pricing and competitive pricing, depending on whether ISP and AP cooperate or compete with each other to make their pricing decisions. Our analysis and simulation results show that all profits of the CP, ISP, AP, and utilities of EUs can be increased by adopting edge cache, compared with the case without edge cache.

Energy harvesting is a promising solution to enable energy sustainable operation of IoT devices. Especially for under-water IoT network as it is difficult and costly for underwater IoT devices to replace the battery. Unlike traditional power supply, energy harvesting from green sources is a random process and is dependent on the charging environment, which poses new challenges for provisioning quality of services of IoT networks. Due to the high cost for low-powered IoT devices to update its energy status with the scheduler, distributed transmission protocol is more desirable for the IoT networks. In this work, we consider an IoT network where IoT devices use adaptive p-persistent ALOHA for data transmissions. Each IoT device can contend for channel access only when it is ready, i.e., it has a data for transmission and it harvests enough energy for communications. Due to stochastic energy harvesting and random access, the number of ready devices in the network may vary. As such, an analytical framework is first developed using a discrete Markov model to analyze the average number of ready devices. Next, an optimization problem is formulated to maximize the system throughput by tuning the transmission probability. Given that the wireless environment is unknown at different IoT devices, e.g., total number of contending devices, data arrival rates of other IoT devices, a multi-agent reinforcement learning algorithm is introduced for each device to autonomously tune the transmission probability in a distributed manner. In addition, game theory is applied to design the reward function to ensure an equilibrium and to closely approach the optimal parameter setting. Numerical results show that the proposed learning algorithm can greatly improve the throughput performance comparing with other algorithms.

As a promising paradigm, VANET has been attracting more and more attention from the industry and academia recently. However, due to rapid movement of vehicles and highly dynamic topology, designing efficient MAC protocol for VANET is still challenging. In this paper, we propose MAC-AC, a novel TDMA-based distributed MAC protocol designed specifically for a vehicular ad hoc network. In MAC-AC, when multiple vehicles compete for the same time slot within their two-hop communication range, one of contending vehicle can obtain this time slot by a simple method, which increases the success probability of vehicles accessing channel. Analysis results are presented to demonstrate the efficiency of MAC-AC and compare it to ADHOC MAC, an existing MAC protocol based on TDMA.

In this paper, we consider the nondeterministic mobility of mobile users, where the platform only has the probability distribution about users' mobility. We design an effective mechanism to achieve high quality data collection with the objective of maximizing the expected social welfare. Simulation results show the better performance of the proposed mechanism compared with four counterparts. In addition, the proposed mechanism also satisfies truthfulness and individual rationality.

Pre-caching popular contents in advance at the network edge such as base stations is a promising method to improve the service quality by reducing the transmission cost and network congestion. In this paper, by jointly considering the mobile users' rating on different contents into the incentive mechanism design, we proposed a rating-aware incentive mechanism for efficiently selecting some BSs to pre-cache the popular contents. The proposed mechanism can achieve higher performance compared with other existing methods in terms of the social welfare. In specific, the proposed mechanism can improve the achieved social welfare by 11.75% and 9.97% compared with the mechanism of random caching and the caching based on bid price, respectively. In addition, the proposed mechanism satisfies the nice properties of individual rationality and truthfulness.

To support massive connectivity and boost spectral efficiency for Internet of Things (IoT), a downlink scheme combining virtual multiple-input-multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to demultiplex the input data streams. Only statistical channel state information (CSI) is available at the base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering the Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of the Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with a higher transmission rate under benign channel conditions.

With the unprecedented development of smart mobile devices (SMDs), e.g., Internet-of-Things devices and smartphones, various computation-intensive applications are explosively increasing in ultradense networks (UDNs). Mobile-edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of SMDs and decrease service latency for computation-intensive applications. With the benefits of network function virtualization, MEC can be integrated with the cloud radio access network (C-RAN) in UDNs for computation and communication cooperation. However, with stochastic computation task arrivals and time-varying channel states, it is challenging to offload computation tasks online with energy-efficient computation and radio resource management. In this article, we investigate the task offloading and resource allocation problem in MEC-enabled dense C-RAN, aiming at optimizing network energy efficiency. A stochastic mixed-integer nonlinear programming problem is formulated to jointly optimize the task offloading decision, elastic computation resource scheduling, and radio resource allocation. To tackle the problem, the Lyapunov optimization theory is introduced to decompose the original problem into four individual subproblems which are solved by convex decomposition methods and matching game. We theoretically analyze the tradeoff between energy efficiency and service delay. Extensive simulations evaluate the impacts of system parameters on both energy efficiency and service delay. The simulation results also validate the superiority of the proposed task offloading and resource allocation scheme in dense C-RAN.

In this paper, we analytically study the performance of opportunistic multi-channel bonding protocol supporting delay-sensitive multimedia services. We consider a multi-channel system shared by IEEE 802.11ac users who can transmit over multiple channels and legacy users who can only transmit over one single channel. By analyzing the channel bonding behavior of IEEE 802.11ac users and the random access of legacy users, bonding probability and successful bonding probability of IEEE 802.11ac users can be derived. Furthermore, the access delays of both legacy and 802.11ac users are analyzed. According to the analytical results, the network capacity which quantifies the maximum number of multimedia flows that can be supported with guaranteed delay is then presented. Additionally, the impacts of different parameters such as traffic data rate on the network capacity are investigated. Our analytical results show that channel bonding is favorable when the secondary channels are underutilized. But channel bonding should be disabled when there are already intense contentions from legacy users. Based on the analytical results, we propose a heuristic bonding policy which can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity. Extensive simulations have been conducted to validate the analytical results.

Driven by the explosive growth in computation-intensive applications in future 5G networks and industries, mobile edge computing (MEC), which enables smart terminals (STs) to offload their computation workloads to nearby edge servers (ESs) in radio access networks, has attracted increasing attention. In this article, we investigate the energy-efficient multitask multiaccess MEC via nonorthogonal multiple access (NOMA). Exploiting NOMA, an ST with multiple tasks can offload the respective computation workloads of different tasks to different ESs simultaneously. To study this problem, we adopt a two-step approach. Specifically, we first consider a given task-ES assignment and formulate a joint optimization of the tasks' computation offloading, local computation-resource allocation, and the NOMA-transmission duration, with the objective of minimizing the ST's total energy consumption for completing all tasks. Next, based on the optimal offloading solution for the given task-ES assignment, we further investigate how to properly assign different tasks to the ESs for further minimizing the ST's total energy consumption. For both the formulated problems, we propose efficient algorithms to compute the respective solutions. Numerical results are provided to validate the effectiveness of our proposed algorithms. The results also show that our proposed NOMA-enabled multitask multiaccess computation offloading can outperform conventional orthogonal multiple access based offloading scheme, especially when the tasks have heavy computation-workload requirements and stringent delay limits.

This paper studies resource allocation for a fully sustainable cooperative network, which consists of multiple Internet of Things (IoT) nodes powered by radio-frequency (RF) energy, one relay with renewable energy supplies, and one destination. Specifically, the relay forwards the data received from IoT nodes to the destination and charges the IoT nodes at the same time. A throughput maximization problem is formulated, which takes into consideration the upper bound of transmit power, stochastic energy harvesting (EH) process and channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different parameters. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

In most of the existing works about mobile crowd sensing, the service provider collects data from each mobile user separately. However, comparing with the collection of data from individual users, batch trading is more attractive for both service provider and mobile users. On one hand, the service provider prefers to buy a batch of data each time even if it may offer a higher unit price since batch trading can save time and efforts in data collection. On the other hand, batch trading is profitable for mobile users since they can take advantage of volume premium. In this paper, we study how mobile users form a coalition to sell their sensing data together. Based on the concept of majorization, we propose a novel scheme to form a fair and stable coalition. Simulation results show the super performance of the proposed method compared with alternative solutions. In specific, the proposed scheme can improve the achieved utility and fairness by 623.68% and 5.51%, respectively, compared to the scheme with independent sell when the number of users is 90.

Device-to-device (D2D) communication is efficient in traffic offloading in terms of transmission rate, energy cost, spectrum usage and etc. However, its unstable performance caused by spectrum reuse can drive users away, particularly when D2D users (DUs) are served as secondary users in cellular networks. Their low expectations on D2D services and frequent mode switch can block efficient D2D offloading, cause severe network congestion and result in poor quality of experience (QoE), especially in densely populated area. Hence, in this study, more than reducing the price to attract users, we propose a quality of service (QoS)- insured pricing strategy to promote D2D communication. In specific, to model users' decision-making process under the uncertainty of D2D performance, we introduce Prospect theory (PT) that captures irrational factors such as reference point, probability distortion and risk-aversion. Then, we design an insurance contract with terms of service price and insured QoS level to popularize D2D services and optimize operator's profit. Based on theoretical analysis, we propose an optimal QoS-insure pricing strategy algorithm, and discuss the practical implementation of the proposed strategy. The simulation results demonstrate the impacts of operation settings, individual irrationality and network conditions on operator's net profit from D2D offloading, and show the efficiency of the proposed strategy.

With the significant advance of vehicle-to-everything (V2X) techniques, unsignalized intersection coordination has been widely recognized to facilitate the development of automated vehicles (AVs) for the intelligent transportation system. However, how to guarantee driving safety while improving the unsignalized intersection management efficiency is a challenging issue. In this article, we investigate the collision-free and efficient V2X-enabled AV scheduling problem at unsignalized intersections. First, by dividing the intersection zone into different collision sections (CSs), we formulate the intersection collision-free model into an absolute value programming (AVP) problem, which is proved to be NP-hard. We consider both nonplatoon and platoon traffic scenarios, and unlike previous algorithms, which require to control all the AVs at each scheduling step with computational intractability, our scheduling algorithm can assign a feasible time for each arriving AV with low complexity. Further, we propose an alternately iterative descent method (AIDM) to solve the AVP problem by assigning the optimal entering time for each arriving AV. Through extensive simulations with various traffic data generated by SUMO, we demonstrate that our proposed AIDM algorithm can significantly enhance the scheduling performance in terms of passing delay and scheduling throughput. Even though the AIDM algorithm achieves the same level of transportation performances with the state-of-the-art algorithm, it advances dramatically in computational complexity and communication overhead, which is easier to be implemented in practice.

Date offloading is an efficient way to address the mobile traffic congestion by redirecting some traffic of a cellular network to complimentary networks. Meanwhile, multicast applications (e.g., video conference, IPTV, and the bulk software upgrade) are very popular. However, few of existing work investigates incentive mechanism design for multicast applications in mobile data offloading. In this paper, we focus on the multicast incentive mechanism design and propose a social-aware multicast incentive mechanism (SAMIM) for data offloading, where the cellular operator will offload the data of a multicast group to some WiFi access points (APs), then these APs serve mobile users (MUs) within their coverage area through multicast transmission. Numerical simulations demonstrate the good performance of SAMIM by comparing with IM-only. In specific, SAMIM improves the system social welfare by 20% compared with IMonly with the number of selected APs |W| = 15.

Compared with a traditional fixed sensor network, mobile crowdsensing provides an efficient way to collect sensing data. However, conducting sensing tasks consumes the resources of mobile users (e.g. battery, storage memory, time). Therefore, incentive mechanism design plays a key role in efficiently collecting the sensing data in a mobile crowdsensing system. Most of existing works about the incentive mechanism design simply use a constant to describe the data quality. In this study, the authors focus on the collection of video clips and introduce multiple parameters to evaluate the quality of the collected data. By jointly considering the social relationship of mobile users, they propose a social‐aware incentive mechanism to achieve the full‐view coverage for a target by efficiently collecting video clips. The proposed mechanism satisfies the properties of individual rationality, truthful and computational efficiency. Simulation results show better performance of the proposed mechanism compared with random selection and aspect based selection. In specific, with the number of users , the proposed mechanism can improve the data collector's utility by 485% and 33% compared with random selection and aspect based selection, respectively.

In a delay-sensitive mobile crowdsensing (MCS) platform, a service provider offers monetary incentives to mobile users for participating in the data collection and reporting their obtained data by a deadline. One aspect missing from most prior literature in the incentive mechanism design is the consideration of the detailed data reporting process through cellular or Wi-Fi networks. In this paper, we consider the interactions between the service provider and the users in two stages. First, the service provider chooses a reward to maximize its expected profit under the incomplete information of the users' responses. Next, given the reward, each user makes his participation and reporting decisions, which are complicated due to his mobility and network heterogeneity. We propose an algorithm to compute the optimal user's decisions under the general setting using dynamic programming, and derive closed-form decision criteria for the special yet practical case of a non-discounted reward. We compute the optimal reward by characterizing the solution set and the discontinuity in the profit function. Simulation results show that our proposed algorithm achieves a significant gain in the user payoff over three benchmark heuristic schemes. In addition, a service provider's profit is sensitive to the estimation of the users' Wi-Fi availabilities.

Machine-to-machine (M2M) communications and applications are expected to be a significant part of the Internet of Things (IoT). However, conventional network gateways reported in the literature are unable to provide sustainable solutions to the challenges posted by the massive amounts of M2M communications requests, especially in the context of the IoT for smart cities. In this paper, we present an admission control model for M2M communications. The model differentiates all M2M requests into delay-sensitive and delay-tolerant first, and then aggregates all delay-tolerant requests by routing them into one low-priority queue, aiming to reduce the number of requests from various devices to the access point in the IoT for smart cities. Also, an admission control algorithm is devised on the basis of this model to prevent access collision and to improve the quality of service. Performance evaluations by network calculus, numerical experiments, and simulations show that the proposed model is feasible and effective.

With a growing demand for mobile data usage, cellular networks are facing the challenge of severe traffic overload. Device-to-Device (D2D) multicast communication, a proximity communication technique that leverages the spatial-temporal locality of mobile data usage to achieve one-to-many simultaneous transmission, provides an efficient solution to offloading heavy traffic. However, D2D multicast communication relies on users' sharing behavior, and multicasting data incur costs such as energy, which prevents the popularity of such user-driven technique. Thus, in this paper, we study the problem of incentive design for promoting D2D multicast communication in cellular networks. Specifically, we propose a contract-based incentive mechanism to optimize the operator's expected profit from motivating D2D multicast communication for content sharing with guaranteed service quality. We consider both complete and incomplete information scenarios. The proposed mechanism can provide efficient incentives under information asymmetry by delivering contracts, which satisfy nice properties such as individual rationality and incentive compatibility. Greedy algorithms with low complexity are developed based on local optimization to obtain fast solutions for contract design. A Lagrange multiplier method based iterative algorithm that can be proved to obtain optimal contracts under information asymmetry is also proposed. Numerical results show that the proposed mechanism can handle information asymmetry better and has a better performance than linear and step pricing schemes, increasing the expected profit by up to 2.49 times and 1.8 times, respectively.

With the explosive increase of mobile traffic in recent years, cellular networks face enormous challenges in high quality of service (QoS) provisioning for mobile users. Mobile data offloading is a promising way to address this issue, through which a cellular system can reduce its traffic burden by offloading some portion of data to other networks, such as Wi-Fi. However, when the Wi-Fi networks are deployed by different operators, an efficient incentive mechanism is needed to encourage the participation of these networks. However, most of the existing studies on the incentive mechanism design focus on the amount of data offloading from the cellular network, rather than the diverse data patterns and features of different applications. In this paper, we propose a QoS-based incentive mechanism, termed QBIM, to promote the cooperation of multiple offloading networks while achieving high QoS level for mobile users with different applications. Through this mechanism, the cellular network chooses the Wi-Fi access points to offload data traffic of mobile users by jointly considering access point operators' bid vectors and the mobile user utilities of different services. Meanwhile, the corresponding payments are designed as the compensation to the involved Wi-Fi systems. The proposed incentive mechanism can not only achieve the maximum social welfare, but satisfy desirable properties of individual rationality and truthfulness as well. Simulation results show that the proposed mechanism achieves a higher utility with a smaller cost compared to the other counterparts.

Machine-to-machine (M2M) communications and applications are expected to be a significant part of the next generation 5G networks. While there have been a large amount of research studies with respect to radio resource management, load balancing, and devices grouping for M2M communications, few of them has addressed the issue of admission control. In this paper, we propose a new admission control model for M2M communications, which classifies all M2M requests into delay-sensitive and delay-tolerant first, and then aggregates all delay-tolerant requests, aiming to reduce the number of requests from devices to base stations. An admission control algorithm based on this model is devised to prevent congestion and to improve the quality of services, and a network calculus based performance-analyzing technique is developed for this model. Both theoretical analyses and simulation results show that the proposed model is feasible and valid.

In a mobile crowdsensing (MCS) application, user diversity and social effect are two important phenomena that determine its profitability, where the former improves the sensing quality, while the latter incentives the users' participation. In this paper, we consider a reward mechanism design for the service provider to achieve diversity in the collected data by exploiting the users' social relationship. Specifically, we formulate a two-stage decision problem, where the service provider first optimizes its rewards for profit maximization. The users then decide their effort levels through social network interactions as a participation game. The analysis is particularly challenging due to the users' interplay in both the diversity and social graphs, which leads to a non-convex bilevel optimization problem. Surprisingly, we find that the service provider can focus on one superimposed graph that incorporates the diversity and social relationship and compute the optimal reward as the Katz centrality in closed-form. Simulation results, based on the random graph and a real Facebook trace, show that the availability of network information improves both the service provider's profit and the users' social surplus over the incomplete information cases.

An analytical model is developed to evaluate the network performance of an IEEE 802.11ac Wireless Local Area Network (WLAN) in support of delay sensitive video services over multiple channels. Specifically, the channel bonding probability and the channel access delay of wireless users are analyzed, considering the contentions among legacy and ac users in the same channel and across multiple channels. Based on the analysis, the network capacity region, i.e., the maximum number of traffic flows can be supported with the bounded delay performance in a multi-channel WLAN with and without channel bonding, is then derived. Our analysis shows that channel bonding can greatly improve the network capacity when the channel is under-utilized with a small number of legacy users co-existing with the ac users; yet channel bonding is not always favorable and it may degrade the network capacity when the number of legacy users increases due to the increased contentions in the network. The analysis provides important guidance for effective admission control and channel bonding strategies to guarantee the bonded service delay of realtime applications. Extensive simulations validate the analysis.

The limited computing ability of mobile device constrains its performance on complex mobile applications. Mobile cloud computing (MCC) has therefore emerged to migrate computation-intensive tasks to remote clouds or mobile cloudlets. Most strategies allocate tasks with minimal response time yet few consider the load of the nodes. In this paper, we focus on the load balancing problem for nodes when they conduct offloading. We first establish a five-tuple characterized task model to capture the response time of offloaded tasks. Then, we formulate the task allocation problem as an integer linear problem (ILP) under certain conditions. Furthermore, we propose a two-step appointment- driven strategy to solve this problem with minimal task response time. Specifically, a modified genetic algorithm (GA) is adopted to coordinate the load of the nodes. Simulations are conducted to prove the feasibility of our strategy and evaluate the performance of load coordination.

The authors take the quality of sensing data into consideration and design a reputation‐aware incentive mechanism (RAIM) with the properties of truthfulness and individual rationality while maximising the weighted social welfare of the whole system. In addition, in order to reduce the computational complexity of RAIM and improve the system feasibility, the authors propose a heuristic algorithm RAIM‐H, with the computational complexity of . Simulation results show the nice performance of the proposed mechanisms RAIM and RAIM‐H in terms of the weighted social welfare and the average reputation. Specifically, RAIM can improve the weighted social welfare by 8.65 and 48.16% compared with trustworthy sensing for crowd management (TSCM) and random selection, respectively, with the number of smartphone users . Meanwhile, RAIM‐H approaches to the maximum very well and can improve the weighted social welfare by 6.15% and 75% compared with TSCM and random selection, respectively, with the number of smartphone users .

Intelligent network selection plays an important role in achieving an effective data offloading in the integrated cellular and Wi-Fi networks. However, previously proposed network selection schemes mainly focused on offloading as much data traffic to Wi-Fi as possible, without systematically considering the Wi-Fi network congestion and the ping-pong effect, both of which may lead to a poor overall user quality of experience. Thus, in this paper, we study a more practical network selection problem by considering both the impacts of the network congestion and switching penalties. More specifically, we formulate the users' interactions as a Bayesian network selection game (NSG) under the incomplete information of the users' mobilities. We prove that it is a Bayesian potential game and show the existence of a pure Bayesian-Nash equilibrium that can be easily reached. We then propose a distributed network selection (DNS) algorithm based on the network congestion statistics obtained from the operator. Furthermore, we show that computing the optimal centralized network allocation is an NP-hard problem, which further justifies our distributed approach. Simulation results show that the DNS algorithm achieves the highest user utility and a good fairness among users, as compared with the on-the-spot offloading and cellular-only benchmark schemes.

To improve the performance of mobile video delivery, caching layered videos at a site near to mobile end users (e.g., at the edge of mobile service provider's backbone) was advocated because cached videos can be delivered to mobile users with a high quality of experience, e.g., a short latency. How to optimally cache layered videos based on caching price, the available capacity of cache nodes, and the social features of mobile users, however, is still a challenging issue. In this paper, we propose a novel edge caching scheme to cache layered videos. First, a framework to cache layered videos is presented in which a cache node stores layered videos for multiple social groups, formed by mobile users based on their requests. Due to the limited capacity of the cache node, these social groups compete with each other for the number of layers they request to cache, aiming at maximizing their utilities while all mobile users in each group share the cost involved in the cache of video contents. Second, a Stackelberg game model is developed to study the interaction among multiple social groups and the cache node, and a noncooperative game model is introduced to analyze the competition among mobile users in different social groups. Third, leveraging the backward induction method, the optimal strategy of each player in the game model is proposed. Finally, simulation results show that the proposed method outperforms the exiting counterparts with a higher hit ratio and lower delay of delivering video contents.

As an efficient way to collect sensing data, participatory sensing has been receiving more and more attentions and its applications cover various areas such as traffic control and management, environmental monitoring, etc. In a participatory sensing system, Service Provider (SP) works as the task promulgator, Smartphone User (SU) works as the task executor and the Platform handles the sensing task allocation procedure. Incentive mechanism plays a key role in stimulating both SPs and SUs to take part in the participatory sensing system. Most of previous works on the incentive mechanism design do not consider the social relationship of SUs, which may degrade the performance of the participatory sensing system. In this paper, we take the social relationship of SUs into consideration, and design a Social-Aware Incentive Mechanism (SAIM) which can achieve high system performance and satisfy the properties of individual rationality, budget balance, the completely truthful for SPs, and partially truthful for SUs. Simulation results show the better performance of the proposed incentive mechanism compared with two other counterparts in terms of social utility and social effect. Specifically, the proposed mechanism can improve the social utility by 12% and 16% compared with McAfee and random selection, respectively, with the number of smartphone users m = 100.

Cellular networks are now facing severe traffic overload problems due to the explosive growth of mobile data traffic. One of the promising solutions is to offload part of the traffic through WiFi. In this paper, we investigate an oligopoly offloading market, where several Mobile Virtual Network Operators (MVNOs) compete to serve end users using the network infrastructure leased from the host Mobile Network Operator (MNO) at the wholesale market. First, we study the competitive interactions among the MVNOs considering the overload problems of the offloading market. Specially, we formulate the interactions as a non-cooperative inventory competition game, where each MVNO determines the amount of cellular traffic it can provide to end users (named as the traffic inventory of each MVNO in this paper) simultaneously. We analyze and derive the existence of the Cournot equilibrium using game theory. Furthermore, we study the impact of the MNO's wholesale price strategy on the market equilibrium. Based on these analysis, we find the optimal initial inventory strategy for these competitors according to the Cournot equilibrium. Finally, our simulations present the process of achieving the market equilibrium and illustrate the impact of the host MNO to the MVNOs.

Various types of sensors have been embedded in smartphones such that a mobile user can easily conduct some sensing tasks. The mobile users conducting the sensing task with their sensor-equipped smartphones have their own unique features, thus can be efficiently complementary to stationary sensors which are deployed at specific locations. In this paper, we consider a heterogeneous sensor network composed of stationary sensors and mobile sensors (i.e., mobile users with sensor-equipped smartphones), in which a key question is how the service provider selects the sensors to conduct the sensing task considering the heterogeneity of sensors in terms of location, mobility pattern, energy constraint, and sensing cost. We propose a greedy algorithm GSSA to reduce the computational complexity. Simulation results show the nice performance of the proposed algorithms compared with the optimal sensor selection algorithm using dynamic programming. In specific, the proposed GSSA improves the achieved social welfare by 32.3% and 35.6% with the time period T=20 for high mobility and low mobility patterns, respectively, compared with the random selection.

Abstract Distributed caching‐empowered wireless networks can greatly improve the efficiency of data storage and transmission and thereby the users' quality of experience (QoE). However, how this technology can alleviate the network access pressure while ensuring the consistency of content delivery is still an open question, especially in the case where the users are in fast motion. Therefore, in this paper, we investigate the caching issue emerging from a forthcoming scenario where vehicular video streaming is performed under cellular networks. Specifically, a QoE centric distributed caching approach is proposed to fulfill as many users' requests as possible, considering the limited caching space of base stations and basic user experience guarantee. Firstly, a QoE evaluation model is established using verified empirical data. Also, the mathematic relationship between the streaming bit rate and actual storage space is developed. Then, the distributed caching management for vehicular video streaming is formulated as a constrained optimization problem and solved with the generalized–reduced gradient method. Simulation results indicate that our approach can improve the users' satisfaction ratio by up to 40%. Copyright © 2015 John Wiley & Sons, Ltd.

A full duplex amplify-and-forward relay network is thoroughly investigated in this paper. To be practical and general, loop interference and Nakagami-m fading channels are considered. Outage probability and ergodic capacity are particularly analyzed and exactly derived in close-forms. Asymptotic outage probability and bounds of the ergodic capacity are also derived in simple forms with clear insights. The analytical results unveil the impacts of loop interference and other system parameters in details. Specifically, it is found that the capacity and outage performance are enhanced via the increase of transmit power at the source, the average fading power and fading order associated with the source-to-relay link. However, under high signal-to-noise ratio (SNR), the increases of the transmit power at the relay and the average fading power corresponding to the loop interference would deteriorate the capacity and outage performance. The results also show that the outage probability obeys an inverse-m1law with respect to the ratio between the transmit powers in the source and relay, where m1is the fading order corresponding to the source-to-relay Nakagami fading channel. The accuracy of the analytical results is validated by Monte-Carlo simulations and they thus can serve as solid foundation for system design and optimization.

By taking advantage of active radio waves, radar sensors can provide high-accuracy target detection over traditional passive sensors. In this paper, we study barrier coverage in bistatic radar sensor networks (BRSNs), which consist of a set of transmitter radars and receiver radars. Barrier coverage in BRSNs is much more difficult than that in traditional sensor networks as the sensing area of a bistatic radar depends on the positions of both transmitter and receiver, and is typically a Cassini oval. Moreover, different transmitters and receivers can pair with each other by choosing the same frequency and thus the sensing network topology can be quite different in different time slots. To tackle this challenge, we first investigate the characteristic of the ε-covered area of a bistatic radar, then we represent a bistatic radar with a virtual point at the middle point of the line segment formed by the transmitter and receiver. With these representations, we formulate the barrier coverage problem in BRSNs as (k, ε)-Minimum Weight Barrier Coverage Problem ((k, ε)-MWBCP). By constructing a directed coverage graph, we transform the (k, ε)-MWBCP into finding k node-disjoint shortest paths and propose an energy-efficient algorithm called (k, ε)-MWBCA to solve the problem within polynomial time. Extensive simulations are conducted to demonstrate the performance of our proposed algorithm.

Providing an adequate long-term user participation incentive is important for a participatory sensing system to maintain enough number of active users (sensors), so as to collect a sufficient number of data samples and support a desired level of service quality. In this work, we consider the sensor selection problem in a general time-dependent and location-aware participatory sensing system, taking the long-term user participation incentive into explicit consideration. We study the problem systematically under different information scenarios, regarding both future information and current information (realization). In particular, we propose a Lyapunov-based VCG auction policy for the on-line sensor selection, which converges asymptotically to the optimal off-line benchmark performance, even with no future information and under asymmetry of current information. Extensive numerical results show that our proposed policy outperforms the state-of-art policies in the literature, in terms of both user participation (e.g., reducing the user dropping probability by 25% ~ 90%) and social performance (e.g., increasing the social welfare by 15% ~ 80%).

With the rich set of embedded sensors installed in smartphones and the large number of mobile users, we witness the emergence of many innovative commercial mobile crowdsensing applications that combine the power of mobile technology with crowdsourcing to deliver time-sensitive and location-dependent information to their customers. Motivated by these real-world applications, we consider the task selection problem for heterogeneous users with different initial locations, movement costs, movement speeds, and reputation levels. Computing the social surplus maximization task allocation turns out to be an NP-hard problem. Hence we focus on the distributed case, and propose an asynchronous and distributed task selection (ADTS) algorithm to help the users plan their task selections on their own. We prove the convergence of the algorithm, and further characterize the computation time for users' updates in the algorithm. Simulation results suggest that the ADTS scheme achieves the highest Jain's fairness index and coverage comparing with several benchmark algorithms, while yielding similar user payoff to a greedy centralized benchmark. Finally, we illustrate how mobile users coordinate under the ADTS scheme based on some practical movement time data derived from Google Maps.

Device‐to‐device (D2D) communication has recently attracted much research attention because of its potential to increase the capacity of cellular networks. Most existing works aim to maximise the overall system throughput (system‐centric), which ignores the actual traffic demands of D2D users. In this study, the authors consider user‐centric relay assisted D2D communications where D2D users have different evaluations for the significance of every unit of increased data rate. By considering the traffic demands of D2D users, the authors propose a Vickrey–Clarke–Groves auction based relay allocation mechanism (ARM) in which every D2D user submits a bid to the basestation (BS). The submitted bids indicate D2D users’ valuation on every unit of the increased data rate. The BS then allocates relays to D2D users by maximising the social welfare of D2D users while maintaining a predefined data rate requirement for cellular users. A payment scheme to charge D2D users for using relays is designed, and the authors show that the auction is truthful. The authors also extend the results to a general case and provide a general ARM accordingly. Extensive simulation results are provided to demonstrate the performance of the proposed mechanisms.

Driven by the ever-growing expectation of ubiquitous connectivity and the widespread adoption of IEEE 802.11 networks, it is not only highly demanded but also entirely possible for in-motion vehicles to establish convenient Internet access to roadside WiFi access points (APs) than ever before, which is referred to as Drive-Thru Internet. The performance of Drive-Thru Internet, however, would suffer from the high vehicle mobility, severe channel contentions, and instinct issues of the IEEE 802.11 MAC as it was originally designed for static scenarios. As an effort to address these problems, in this paper, we develop a unified analytical framework to evaluate the performance of Drive-Thru Internet, which can accommodate various vehicular traffic flow states, and to be compatible with IEEE 802.11a/b/g networks with a distributed coordination function (DCF). We first develop the mathematical analysis to evaluate the mean saturated throughput of vehicles and the transmitted data volume of a vehicle per drive-thru. We show that the throughput performance of Drive-Thru Internet can be enhanced by selecting an optimal transmission region within an AP's coverage for the coordinated medium sharing of all vehicles. We then develop a spatial access control management approach accordingly, which ensures the airtime fairness for medium sharing and boosts the throughput performance of Drive-Thru Internet in a practical, efficient, and distributed manner. Simulation results show that our optimal access control management approach can efficiently work in IEEE 802.11b and 802.11g networks. The maximal transmitted data volume per drive-thru can be enhanced by 113.1% and 59.5% for IEEE 802.11b and IEEE 802.11g networks with a DCF, respectively, compared with the normal IEEE 802.11 medium access with a DCF.

In participatory sensing (PS), users use smartphones to collect information related to a certain phenomenon of interest, and report their sensed data to the service provider through cellular or Wi-Fi networks. Previous studies on the incentive mechanism design for user participation often neglect the details of data reporting, which is non-trivial given the user mobility, location-dependent network availability, and transmission cost. In this paper, we study the decisions of the service provider and the users in PS applications that involve photo or video transmissions, where the reporting cost through the cellular network is non-negligible. The service provider uses a deadline reward scheme to motivate users to participate, and optimizes its reward to maximize its expected surplus. Users make their participation and reporting decisions based on the reward announced by the service provider. We jointly consider the user mobility and multiple access methods with different transmission costs and location heterogeneity in the problem formulation and analysis. For the general case with a time-discounted reward, we formulate a user's reporting decision problem as a sequential decision problem, and propose an optimal participation and reporting decisions (OPRD) algorithm using dynamic programming. For the special case with a fixed reward, we derive the closed-form participation and reporting decisions. Simulation results show that the OPRD algorithm improves the user payoff over the patient and impatient schemes by 9.8% and 13.2%, respectively.

The recent advances in wireless communication techniques have made it possible for fast-moving vehicles to download data from the roadside communications infrastructure [e.g., IEEE 802.11b Access Point (AP)], namely, Drive-thru Internet. However, due to the high mobility, harsh, and intermittent wireless channels, the data download volume of individual vehicle per drive-thru is quite limited, as observed in real-world tests. This would severely restrict the service quality of upper layer applications, such as file download and video streaming. On addressing this issue, in this paper, we propose ChainCluster, a cooperative Drive-thru Internet scheme. ChainCluster selects appropriate vehicles to form a linear cluster on the highway. The cluster members then cooperatively download the same content file, with each member retrieving one portion of the file, from the roadside infrastructure. With cluster members consecutively driving through the roadside infrastructure, the download of a single vehicle is virtually extended to that of a tandem of vehicles, which accordingly enhances the probability of successful file download significantly. With a delicate linear cluster formation scheme proposed and applied, in this paper, we first develop an analytical framework to evaluate the data volume that can be downloaded using cooperative drive-thru. Using simulations, we then verify the performance of ChainCluster and show that our analysis can match the simulations well. Finally, we show that ChainCluster can outperform the typical studied clustering schemes and provide general guidance for cooperative content distribution in highway vehicular communications.

The recent advances in wireless communication techniques have made possible for vehicles to download from the roadside communications infrastructure, namely drive-thru Internet. However, due to the fast-motions, harsh and intermittent wireless channels, the download volume of individual vehicles per drive-thru is quite limited as observed in real-world tests. This severely restricts the service quality of upper-layer applications, such as file download and video streaming. To address this issue, we take a historical approach by evaluating the integrated download throughput of a cooperative vehicle group in the highway environment. In specific, we first introduce a practical microscopic vehicular mobility model, which takes the randomness of speed update and safety distance requirement into account. Then, we analyze and formulate the number of contending vehicles within the coverage range of access point (AP) in the single-lane highways scenario, which can also be easily extended into the multi-lane highways scenario. Furthermore, we derive the data download volume by a vehicle per drive-thru, and analyze the relationship between the mobility speed and the data download volume. Finally, we derive the number of cooperative vehicles required for completing a download task in our investigated highways drive-thru Internet. The analytical model and evaluation results provide general guidance for cooperative content distribution and protocol design in drive-thru Internet.

In this paper, we propose a design framework for achieving efficient multimedia multicast services in cognitive radio (CR) networks. The framework incorporates the characteristics of both heterogeneous network environment and the scalable video content. By adopting cooperative transmissions for the delivery of enhancement layer data, we can not only improve the achieved video quality but also protect the rights of subscribed secondary users. We also utilize network coding and superposition coding to achieve efficient multicast transmissions of the layered video packets in multi-channel CR networks. Numerical examples show the proposed framework can improve the average received data rate by up to 15%. When achieving the same video quality, the proposed framework can save 30% transmission time comparing with the scenario using direct transmission alone.

In this paper, we study random access in a drive-thru scenario, where roadside access points (APs) are installed on a highway to provide temporary Internet access for vehicles. We consider vehicle-to-roadside (V2R) communications for a vehicle that aims to upload a file when it is within the APs' coverage ranges, where both the channel contention level and transmission data rate vary over time. The vehicle will pay a fixed amount each time it tries to access the APs, and will incur a penalty if it cannot finish the file uploading when leaving the APs. First, we consider the problem of finding the optimal transmission policy with a single AP and random vehicular traffic arrivals. We formulate it as a finite-horizon sequential decision problem, solve it using dynamic programming (DP), and design a general dynamic optimal random access (DORA) algorithm. We derive the conditions under which the optimal transmission policy has a threshold structure, and propose a monotone DORA algorithm with a lower computational complexity for this special case. Next, we consider the problem of finding the optimal transmission policy with multiple APs and deterministic vehicular traffic arrivals thanks to perfect traffic estimation. We again obtain the optimal transmission policy using DP and propose a joint DORA algorithm. Simulation results based on a realistic vehicular traffic model show that our proposed algorithms achieve the minimal total cost and the highest upload ratio as compared with some other heuristic schemes. In particular, we show that the joint DORA scheme achieves an upload ratio 130% and 207% better than the heuristic schemes at low and high traffic densities, respectively.

In a drive-thru scenario where vehicles drive by a roadside access point (AP) to obtain temporary Internet access, it is important to design efficient resource allocation schemes to fully utilize the limited communication opportunities. In this paper, we study the random access problem in drive thru communications in a dynamic environment, where both the channel contention level and channel capacity vary over time. We assume that a vehicle has a file to upload when it is within the coverage range of the AP. The vehicle will pay a fixed amount each time it tries to access the AP, and will incur a penalty if it cannot finish the file uploading when leaving the AP. We first formulate the optimal transmission problem as a finite-horizon sequential decision problem. Then we solve the problem using dynamic programming, and design a dynamic optimal random access algorithm. Simulation results based on a realistic vehicular traffic model show that our algorithm achieves the minimal total cost, the highest probability of completing file upload, and the highest upload ratio as compared with two other heuristic schemes.

We consider the channel selection problem in a cognitive radio network with heterogenous channel availabilities at different nodes. We formulate the maximum channel selection (MCS) problem as a binary integer nonlinear optimization problem, with an objective of maximizing the total channel utilization for all secondary nodes. We first prove that MCS problem is NP-complete. Then we design a centralized greedy channel selection (GCS) algorithm. The GCS algorithm is polynomial in computational complexity, and achieves a close-to-optimal (higher than 95%) numerical performance. We further propose a distributed priority order channel selection algorithm, which has significantly less signaling overhead compared with the GCS algorithm. We study the performance of the distributed algorithm both theoretically and numerically.

Abstract In IEEE 802.16 networks, a subscriber station (SS) could be a single mobile user, a residence house, or an office building providing Internet service for multiple customers. Considering the heterogeneity among SSs which have diverse traffic demands, in this paper, we introduce the weighted proportional fair (WPF) scheduling scheme for the Best Effort (BE) service in IEEE 802.16 networks to achieve the flexible and efficient resource allocation. We develop an analytical model to investigate the performance of WPF in terms of spectral efficiency, throughput, resource utilization, and fairness, where the Rayleigh fading channel and the adaptive modulation and coding (AMC) technique are considered. Extensive simulations are conducted to illustrate the efficiency of the WPF scheduling scheme and verify the accuracy of the analytical model. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we propose an adaptive forwarding scheme for achieving efficient message delivery in the mobile sensor delay-tolerant network (DTN). By adaptively adjusting the probability of replicating a message based on the information about the number of existing message copies in the network, the proposed scheme can achieve nice balance between the delivery delay and the average message copy. In addition, by decreasing the number of message copies in the network, the proposed scheme can reduce the network resource consumption in terms of buffer occupation and power consumption.

Multicast communications is an efficient mechanism for one-to-many transmissions over a broadcast wireless channel, and is considered as a key technology for supporting emerging broadband multimedia services in the next generation wireless networks, such as Internet Protocol Television (IPTV), mobile TV, etc. Therefore, it is critical to design efficient multicast scheduling schemes to support these multimedia services. In this paper, we propose a cooperative multicast scheduling scheme for achieving efficient and reliable multicast transmission in IEEE 802.16 based wireless metropolitan area networks (WMAN). By exploiting the multi-channel diversity across different multicast groups and user cooperation among group members, the proposed scheme can achieve higher throughput than existing multicast schemes, for subscriber stations in both good and bad channel conditions. In addition, it has good fairness performance by considering the normalized relative channel condition of each multicast group. An analytical model is developed to evaluate the performance of the proposed scheme, in terms of service probability, power consumption, and throughput of each group member and multicast groups. The efficiency of the proposed scheme and the accuracy of the analytical model are corroborated by extensive simulations.

This paper proposes an efficient yet simple design framework for achieving flexible resource allocation and packet scheduling for non-real-time polling service (nrtPS) traffic in IEEE 802.16 networks. By jointly considering the selective automatic repeat request mechanism at the media access control layer as well as the adaptive modulation and coding technique at the physical layer, the proposed framework enables a graceful tradeoff between resource utilization and packet delivery delay while maintaining the minimum throughput requirements of nrtPS applications. An analytical model is developed for parameter manipulation in the proposed framework, where some important performance metrics, such as inter-service time, delivery delay, goodput, and resource utilization, are investigated for performance evaluation. Simulation results are given to demonstrate the efficiency of the proposed framework and verify the accuracy of the analytical model.

Exploiting the broadcast nature of wireless communications, multicast transmission is an efficient way to improve the network throughput by transmitting the same contents to multiple receivers simultaneously. It has been considered as a key technology for supporting emerging services in next-generation IEEE 802.16 based wireless metropolitan area networks (WMANs), such as Internet Protocol TV (IPTV) and mobile TV. Therefore, it is critical to devise efficient multicast scheduling schemes to support these multimedia services. In this paper, we propose a novel multicast scheduling scheme, using downlink cooperative transmission for achieving high throughput not only for all multicast groups but also for each group member. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.

In IEEE 802.16 networks, a subscriber station (SS) could be a single mobile user, a residence house, or an office building providing Internet service for multiple customers. Considering the heterogeneity among SSs which have different traffic load/demands, in the paper, we introduce the weighted proportional fair (WPF) scheduling scheme for best effort (BE) service in IEEE 802.16 networks to achieve the flexible and efficient resource allocation. Furthermore, an analytical model is developed to investigate the performance of WPF in terms of spectral efficiency, throughput, resource utilization, and fairness. Extensive simulations are conducted to illustrate the efficiency of the proposed scheme and verify the accuracy of the analytical model.

A cross-layer design (CLD) framework for robust and efficient video multicasting over IEEE 802.16 (or WiMAX) is introduced. In the framework, multiple description coding on scalable video bitstreams at the source for achieving multi-resolution robustness is jointly designed with superposition coding (i.e., multi-resolution modulation) on multicast signals at the channel to overcome the channel diversity problem in wireless multicast. The resulting cross-layer coded multicast signals enable us to recover some lost bitstreams in high quality layers, which is not possible if multi-resolution modulation is used alone for multicasting as in previous works. Simulation results show that indeed our joint design outperforms the scheme using only superposition coded multicast by achieving better video quality for users under multi-user channel diversity.

In this paper, we propose an application-driven MAC-layer buffer management framework based on a novel active dropping (AD) mechanism for real-time video streaming in IEEE 802.16 Point-to-Multi-Point (PMP) networks. The basic idea of the proposed approach is that the MAC-layer protocol data units (MPDUs) of a video stream could be actively dropped at the Base Station (BS) if the corresponding frame is not with a sufficient confidence to be successfully delivered to the recipient within its application-layer delay bound. In contrast to the conventional cross-layer techniques that manipulate transmission and/or retransmission priorities for sending MPDUs of a single stream, the proposed AD mechanism can be more effectively bound the delay of each video frame and release precious transmission resources for the subsequent frames or the frames of the other competing streams. This is considered as an intelligent approach for minimizing delay propagation due to bad channels or any other possible reason. A comprehensive analytical model is formulated on deriving how confident a frame can be effectively delivered within its application-layer delay bound by jointly considering the effect of playback buffering. Extensive simulation is performed to demonstrate the effectiveness of the proposed scheme. We expect that the proposed application-driven MAC-layer buffer management can incorporate with the emerging cross-layer design paradigm for real-time video streaming in TDMA-based wireless broadband access networks such as IEEE 802.16.

As a promising broadband wireless access standard, IEEE 802.16 specified some advance physical layer techniques and media access control layer protocols, which pose many fundamental differences in terms of automatic repeat request (ARQ) mechanism, scheduling scheme, and resource allocation, compared with those done in many previous works. In this paper, we analyze the performance of ARQ in IEEE 802.16 networks by jointly considering the opportunistic scheduling scheme, where the delivery delay and goodput are investigated as two performance metrics. Simulation results are given to verify the proposed analysis model.

The paper proposes a distributed connection admission control and resource allocation scheme for IEEE 802.16 networks. Rather than investigating the short-term variation of wireless channel conditions for different sub-carriers, the study focuses on relative long-term capacity planning at the base station on a time-of-a-day basis for each subscriber station and mobile user. With the proposed scheme, each subscriber station performs distributed admission control to decide whether or not to accept connection requests originated by its end users based on the granted capacity aiming at optimizing the overall revenue. A cross-layer design and optimization cooperated with a suite of distributed signaling are addressed and implemented such that the network capacity can be assigned efficiently. Simulation results are given to demonstrate the efficiency of the proposed scheme.

An increasing interest in IEEE 802.16 networks has been witnessed due to the demonstrated unique features in offering quality of service (QoS) satisfaction and service differentiation. Efficient scheduling plays a key role in fulfilling these operational requirements and unique characteristics. Traditional scheduling schemes such as opportunistic scheduling and proportional fairness scheduling focuses on throughput maximization and fairness instead of service differentiation and QoS guarantee. In this paper, a novel scheduling scheme is proposed to provide the QoS satisfaction and service differentiation in terms of delay. The proposed scheme manipulates a new design parameter - the time window of throughput evaluation, to differentiate the delay performance of each queue. By embedding wireless channel condition of each queue into the preference metric, opportunistic scheduling can be properly realized by trading the delay performance. An analytical model is developed on inter-service time, queue length, and waiting time, and is verified through extensive simulation.

The integration of the third generation (3G) wireless wide-area networks (WWAN) and the IEEE 802.11 wireless local-area networks (WLAN) has drawn much attention from both industry and academia. To achieve an effective and efficient integration between the two networks with very different characteristics, nonetheless, is still an open issue. One of the challenges is to provide an integrated strategy for achieving a seamless vertical handoff of mobile users roaming between the two network domains where the delay, delay jitter, and packet loss probability can be well controlled. This paper is committed to study a two-step vertical handoff mechanism based on linear regression which is further modeled through an analytical approach. The proposed vertical handoff scheme is characterized by its adaptability to different quality of service (QoS) requirements by manipulating a threshold on the expected handoff instant. A new approach of mobility analysis is introduced to facilitate modeling of vertical handoff delay by taking advantage of Markov chain techniques. We have seen merits gained in our scheme in achieving a good trade-off between the average handoff delay and the multi-tunnel time by manipulating a threshold value, where both analytical and simulation results prove the effectiveness.

There is an increasingly growing interest in the IEEE 802.16 owing to its unique and useful characteristics in offering broadband wireless access. One of the key strengths is its support for different quality of service (QoS) classes which positions it to support high-quality voice, video, and data services. Connection admission control (CAC) plays an important role in fulfilling service differentiation and QoS satisfaction defined in IEEE 802.16 Std.. A traditional CAC scheme, known as complete sharing, is expected unable to explore the maximum advantage in using the IEEE 802.16 networks since it does not take the priority of different service classes into account. In this paper, a reservation based CAC scheme is introduced. By considering the service differentiation defined in the IEEE 802.16 networks, the proposed scheme can provide significantly lower connection block probabilities for higher priority services, which leads to better revenue. We analyze the proposed scheme in terms of some importance performance metrics, such as connection block probability for different service classes, bandwidth utilization, and the revenue generated at the BS. The analysis and simulation results are given to illustrate the efficiency of the proposed scheme and the accuracy of the analysis.

Additive Increase Multiplicative Decrease (AIMD) window adjustment mechanism has been embedded in TCP in order to regulate the transmission rate in modern communication networks. In recent years, the AIMD (1,0.5) traffic regulation mechanism along with possibly additional enhancements, such as false timeout detection and explicit notification, has been considered in the carriers with Optical Burst Switching (OBS) as the underlying transmission technology. This paper introduces a novel rate control mechanism based on Generalized AIMD (α,β), called Burst AIMD (BAIMD), for tuning the rate control parameters (α, β) at each sender. BAIMD is designed to improve throughput while maintaining friendliness with co-existing AIMD (1,0.5) flows, and is characterized in the following two folds: (1) no burst window is required in the TCP sender's level; (2) no explicit notifications are required. The above characteristics make the proposed scheme distinguished from all the past reported counterparts by minimizing the signalling efforts and control complexity. The simulation result shows that BAIMD can solidly outperform the past reported AIMD-based (1,0.5) rate control schemes under a wide range of traffic loads. We also suggest that BAIMD rate control mechanism may serve as a better choice than AIMD (1,0.5) in the bufferless OBS networks due to its dynamic and flexible (α,β) parameter pair.

In this paper, the performance of unsolicited grant service (UGS) connections defined in IEEE 802.16 Std. is investigated. A simple admission control strategy based on the periodic exhaustive service principle is first introduced to provide Quality of Service (QoS) satisfaction for such connections. The task of system parameter selection and performance evaluation for the UGS flows is tackled. In particular, the maximum retransmission limit that works along with the embedded Automatic Repeat reQuest (ARQ) mechanism is emphasized. A novel transferred model is formulated such that the lossy characteristic of the wireless channels in real networks can be completely engineered by way of the traffic arrival pattern and the size of the messages in the proposed model. Significant merits and efficiency have been identified in the proposed model through extensive simulation, where a complete match between the analytical and simulation results is observed.